What is the process working on Tensorflow model.fit()?
You can save weights during training by passing checkpoint callback to model.fit() method.
# Instantiate your model here
model = create_model() 

# Set model configurations here
model.compile(loss=..., optimizer=..., metrics=...) 

# Set checkpoint path
checkpoint_path = &quot;model_weights.ckpt&quot;

# Create a callback that saves the model's weights
    filepath=checkpoint_path,
    save_weights_only=True,
    monitor='val_loss',
    mode='min',
    save_best_only=True)

# Train the model with the new callback
model.fit(train_images_1, 
          train_labels_1,
          epochs=50, 
          batch_size=batch_size, 
          callbacks=[cp_callback],
          validation_data=(test_images_1, test_labels_1),
          verbose=0)

After finishing training 1st dataset, model weights will be saved in file called model_weights.ckpt. Before starting training next dataset, load the model weights as below
# Create a new model instance
model = create_model()

# Set model configurations here
model.compile(loss=..., optimizer=..., metrics=...) 

# Set checkpoint path
checkpoint_path = &quot;model_weights.ckpt&quot;

# Load the previously saved weights
model.load_weights(checkpoint_path)

# Create a callback that saves the model's weights
cp_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_path,
    save_weights_only=True,
    monitor='val_loss',
    mode='min',
    save_best_only=True)

# Train the model with the new callback
model.fit(train_images_2, 
          train_labels_2,
          epochs=50, 
          batch_size=batch_size, 
          callbacks=[cp_callback],
          validation_data=(test_images_2, test_labels_2),
          verbose=0)

Repeat this for all datasets.